The invention relates to a method and system for detecting a fiducial in digital projection images. In particular, the invention relates to a method, system and software product for automatically detecting a fiducial in digital X-ray images without any operations required of the user.
In the majority of medical imaging, the finding of certain locations in the image is particularly important, for instance in a situation where images are taken by various different methods, such as by X-ray, ultrasound and/or magnetic imaging equipment, and where a given anatomic spot of the patient, or a fiducial attached to the patient prior to the imaging process, must be located for example in order to mutually focus different images on the same spot, or in a situation where the operation is connected to a given anatomic spot, the location of which is otherwise difficult to distinguish in the image. It is also known to mutually focus images taken with the same modality but at different times. Fiducials can also be otherwise used for getting additional information of projection images, for example after the imaging operation proper, for instance in order to analyze imaging geometry, in order to track and compensate possible motions of the patient and/or in order to calculate a TACT reconstruction (Tuned Aperture Computed Tomography).
The use of artificial fiducials is prior art technology in medical imaging, where fiducials have typically been detected in an image by the manual “point-and-click” principle. The detection of various pointers, figures or patterns in an image represents prior art technology also as regard various machine vision applications. For example publication [1] illustrates an arrangement for detecting elliptical paths in an image by utilizing both the three first intensity moments of the image as well as the intensity gradient. In addition, publication [2] suggests a solution for tracking the contours of an ellipse in an image at the frame rate, for example by using visual pointers of the image (gradient and mode) as well as a RANSAC-type probability algorithm. In addition, among solutions of the prior art, it is known to search for a pattern of a given shape by calculating the correlation between the image and the model in order to find out whether an image according to the model is found in the image. Moreover, the performing of a Hough transformation in the parameter space of the image is also known for finding fiducials on the basis of their parameter representations, so that in the parameter space, there is summed up the space-function at the parameter values, and there are looked up the local maximum points of the parameter space, which maximum points correspond to the fiducials to be detected.
In arrangements that are used in prior art medical imaging, fiducials are typically detected manually, so that the user interactively points, for example by the computer mouse, at least approximately to the location of the fiducial, whereafter the fiducial is attempted to be found by means of the software, or as an alternative, the user indicates the location of the fiducial directly by visual estimation. However, the manual detection of fiducial locations is extremely slow, frustrating and susceptible to errors. In addition, the location accuracy of fiducials that are detected completely manually and only by visual estimation is extremely poor, and the variance is high. Moreover, as the user's attention is weakened, the accuracy in defining the locations of fiducials suffers remarkably, in which case fiducials may easily remain undetected in the image.
As regards prior art methods, software methods and others, for detecting fiducials, models or patterns, the problem is their slowness and inaccuracy. Particularly in a search process based on correlation, in case the fiducial image is for example partly covered, the correlation easily falls at a wrong point. Moreover, solutions according to the prior art are sensitive to the image noise. Prior art fiducial detection methods also are typical in that the user must in advance point that region in the image where fiducial images are supposed to be located, such as in the arrangement of publication [1]. In addition, in the solution of publication [2], one and the same target is tracked in successive images according to a two-step “detection and tracking” method, where the “detection” step utilizes a Kalman filter. However, in the solution of publication [2], the detection accuracy of the employed Kalman filter is not sufficient for surveying medical images.